scikit-bio

scikit-bio - Bioinformatics and Ecology

scikit-bio provides the data structures and statistical methods needed for rigorous biological analysis. It excels in calculating alpha/beta diversity, performing ordination (PCoA), and handling complex phylogenetic trees.

When to Use

• Analyzing microbiome data (taxonomic composition, community structure).
• Calculating ecological diversity metrics (Shannon, Simpson, UniFrac).
• Performing ordination for visualization (PCoA, DCA).
• High-level sequence manipulation (DNA, RNA, Protein with metadata).
• Reading and writing phylogenetic trees (Newick format).
• Pairwise and multiple sequence alignment analysis.
• Statistical testing of community differences (PERMANOVA, ANOSIM).

Reference Documentation

Official docs: http://scikit-bio.org/
Tutorials: http://scikit-bio.org/docs/latest/
Search patterns: skbio.sequence, skbio.stats.distance, skbio.diversity, skbio.stats.ordination

Core Principles

Grammar of Biological Sequences

Instead of using raw strings, scikit-bio uses typed objects (DNA, RNA, Protein). These objects know their alphabet, can handle quality scores (Phred), and support biological operations (transcription, translation).

Distance Matrices

Microbiome research often boils down to comparing samples. The DistanceMatrix object is central, allowing for easy indexing, sub-setting, and statistical testing (e.g., PERMANOVA).

Diversity Metrics

Provides a standardized implementation of hundreds of diversity metrics used in ecology, ensuring reproducibility across studies.

Quick Reference

Installation

pip install scikit-bio

Standard Imports

import skbio
import numpy as np
import pandas as pd
from skbio import DNA, RNA, Protein, Sequence
from skbio.stats.distance import DistanceMatrix
from skbio.diversity import alpha, beta
from skbio.stats.ordination import pcoa

Basic Pattern - Sequence Manipulation

from skbio import DNA

# 1. Create a sequence with metadata
seq = DNA("ACC--GTT", metadata={'id': 'sample1', 'desc': 'gene A'})

# 2. Biological operations
rc = seq.reverse_complement()
degapped = seq.degap()

# 3. Validation
print(f"Is valid? {seq.is_valid()}") # Checks against DNA alphabet